Event trigger formation control method, device and equipment of distributed multi-agent system, medium and product
By constructing a locally adaptive event-triggered formation control law, the problems of global information dependence and continuous communication in distributed multi-agent systems are solved, achieving efficient formation control in intermittent communication environments and improving the system's economy and reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-08
- Publication Date
- 2026-03-10
AI Technical Summary
Existing event-triggered formation control methods for distributed multi-agent systems suffer from a trade-off between convergence performance and communication costs. They rely on global information and continuous communication, making it difficult to work effectively in intermittent communication environments.
By constructing an event-triggered formation control law based on local formation error and preset time adaptive gain, and combining it with the communication topology and desired formation of the distributed multi-agent system, local adaptive control is achieved, freeing it from dependence on global information and the limitations of continuous communication.
While significantly saving communication resources, it ensures that the multi-agent system converges accurately within a preset time, improving the system's economy and feasibility, and realizing distributed formation control.
Smart Images
Figure CN121635018A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of event-triggered formation control, and in particular, to an event-triggered formation control method, device, equipment, medium and product of a distributed multi-agent system. BACKGROUND
[0002] With the continuous development of multi-agent system cooperative control theory, it has been widely applied in many fields such as unmanned aerial vehicle cluster and robot cooperation. Through formation control, multi-agent systems can cooperatively complete complex tasks, significantly improving the overall system's task execution efficiency, robustness and environmental adaptability. Therefore, the formation control research of multi-agent systems not only has important theoretical value, but also shows broad engineering application prospects.
[0003] Although many continuous communication-based control protocols can achieve formation, they will continuously consume communication bandwidth and energy, which is difficult to maintain for a long time in actual systems. As an effective improvement scheme, event-triggered control can significantly save communication resources while ensuring smooth transmission of key data. For example, in long-term field monitoring sensor devices, only relying on on-board batteries for power supply, the communication module is the main energy consumption unit, and the use of event-triggered algorithm can effectively save power and ensure important data collection and communication. In addition, in large indoor warehouse robot systems, the communication channel and bandwidth are limited, and the simultaneous operation of dozens of robots can easily cause network congestion. If each robot uses an event-triggered mechanism, it will greatly reduce network load, thereby ensuring low delay and low packet loss rate of communication, and ensuring production safety.
[0004] However, the event-triggered formation control of the distributed multi-agent system involved in the prior art often has a contradiction between convergence performance and communication cost. Specifically, the conventional method that can guarantee stability often has the problems of slow convergence speed and uncertain convergence time; and some preset time control methods aimed at improving the convergence speed usually have high requirements for the continuity of communication, which are difficult to work effectively in limited environments such as intermittent communication. In addition, the existing methods usually rely on global information of the communication topology (such as eigenvalues of Laplacian matrix, number of agents, etc.), which makes it difficult to select controller parameters, which is not conducive to engineering implementation.
[0005] Therefore, based on the above problems, it is necessary to provide an event-triggered formation control method of a distributed multi-agent system, which can get rid of the dependence on global information and the limitation of continuous communication, and realize the formation control of a distributed multi-agent system. SUMMARY
[0006] The purpose of this application is to provide an event-triggered formation control method, device, equipment, medium, and product for a distributed multi-agent system, which can get rid of the dependence on global information and the limitation of continuous communication, and realize formation control of a distributed multi-agent system.
[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides an event-triggered formation control method for a distributed multi-agent system, including: Obtain the communication topology and desired formation of the distributed multi-agent system; Based on the communication topology and the agent state at the current moment, a communication triggering condition is constructed for each agent; the communication triggering condition is used to determine the triggering time; the triggering time is the moment when the current agent sends its corresponding state to its neighboring agents; Based on the agent's state at the triggering moment, the corresponding local formation error, the preset time parameters, and the time-varying adaptive gain, a preset time adaptive event-triggered formation control law is constructed; the local formation error is the difference between the agent's state at the triggering moment and the corresponding desired formation. Real-time formation control is performed based on the dynamic model of a distributed multi-agent system, triggered by a preset time-adaptive event-triggered formation control law.
[0008] Optionally, the intelligent agent includes: a robot, a drone, or an intelligent vehicle.
[0009] Optionally, obtaining the communication topology and desired formation of the distributed multi-agent system specifically includes: Using formula Determine the communication topology of a distributed multi-agent system ; in, For intelligent agents With intelligent agents Communication connection weights between them For the set of real numbers, The total number of intelligent agents.
[0010] Optionally, the step of constructing the communication triggering conditions for each agent based on the communication topology and the agent's current state specifically includes: Using formula Construct communication trigger conditions for each agent ; in, The total number of agents. Number the intelligent agents. For the first Trigger threshold adjustment parameters for each agent For edge-based time-varying adaptive gain, For intelligent agents With intelligent agents Communication connection weights between them For the first The state estimation error of each agent For local formation error, , For the first The state at the trigger moment of an agent For the first The desired formation of each agent For the first The state at the trigger moment of an agent For the first The desired formation of each agent To balance the adjustable parameters of the communication frequency, For preset time parameters, superscript Adjustable parameters for balancing system performance.
[0011] Optionally, the step of constructing a preset time-adaptive event-triggered formation control law based on the agent's state at the triggering moment, the corresponding local formation error, preset time parameters, and time-varying adaptive gain specifically includes: Using formula Construct a pre-defined time-adaptive event-triggered formation control law; in, For intelligent agents The control input, For preset time parameters, The total number of agents. Number the intelligent agents. For intelligent agents With intelligent agents Communication connection weights between them For edge-based time-varying adaptive gain, For local formation error, Let be the derivative of the desired formation.
[0012] Optionally, the expression for the dynamic model of the distributed multi-agent system is: ; in, For intelligent agents The derivative of the state, For intelligent agents Control input.
[0013] Secondly, this application provides an event-triggered formation control device for a distributed multi-agent system, comprising: The communication topology determination module is used to obtain the communication topology and desired formation of the distributed multi-agent system. The communication triggering condition determination module is used to construct the communication triggering condition for each agent based on the communication topology and the agent state at the current moment; the communication triggering condition is used to determine the triggering time; the triggering time is the moment when the current agent sends the corresponding state to the neighboring agents; The control law construction module is used to construct a preset time adaptive event-triggered formation control law based on the agent's state at the triggering moment, the corresponding local formation error, the preset time parameters, and the time-varying adaptive gain; the local formation error is the difference between the agent's state at the triggering moment and the corresponding desired formation. The formation control module is used to trigger the formation control law according to the preset time adaptive event, and to perform real-time formation control based on the dynamic model of the distributed multi-agent system.
[0014] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the event-triggered formation control method for the distributed multi-agent system described above.
[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the event-triggered formation control method for the distributed multi-agent system described above.
[0016] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the event-triggered formation control method for the distributed multi-agent system described above.
[0017] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides an event-triggered formation control method, apparatus, device, medium, and product for a distributed multi-agent system. By acquiring the communication topology and desired formation of the distributed multi-agent system, and constructing communication triggering conditions for each agent based on the communication topology and the agent's current state, the communication triggering is combined with preset time parameters. This significantly saves communication resources while ensuring accurate convergence of the distributed multi-agent system within a preset timeframe. By constructing a preset time-adaptive event-triggered formation control law based on the agent's state at the triggering moment, the corresponding local formation error, the preset time parameters, and the time-varying adaptive gain, the local adaptive design completely eliminates dependence on global information, achieving true distributed control. This application effectively solves the problems of dependence on global information and continuous communication limitations in existing technologies, improving the economy, feasibility, and reliability of formation control for multi-agent systems. It enables formation control of multi-agent systems under intermittent communication conditions without relying on global information of the communication topology, and ensures system convergence within a preset timeframe. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram of the event-triggered formation control method for a distributed multi-agent system in one embodiment of this application; Figure 2 This is a schematic diagram of the communication topology of a multi-unmanned vehicle system in one embodiment of this application; Figure 3 This is a schematic diagram of the location trajectory of a multi-unmanned vehicle system within 20 seconds in one embodiment of this application; Figure 4 This is a schematic diagram of the local formation error convergence process of a multi-unmanned vehicle system in one embodiment of this application; Figure 5 This is a schematic diagram of the triggering time of the unmanned vehicle in one embodiment of this application. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] In one exemplary embodiment, such as Figure 1 As shown, an event-triggered formation control method for a distributed multi-agent system is provided, including the following S1 to S4. Wherein: S1: Obtain the communication topology and desired formation of the distributed multi-agent system.
[0023] A distributed multi-agent system consists of multiple agents with mobility and communication capabilities, including but not limited to robots, drones, or intelligent vehicles. The agents within the distributed multi-agent system can exchange information through a communication network. S1 specifically includes: S11: Obtain the communication topology of the distributed multi-agent system.
[0024] In a distributed multi-agent system, agents communicate and exchange information in real time to achieve overall formation tracking using local information. The communication topology of a distributed multi-agent system is described below. As shown below: ; in, For intelligent agents With intelligent agents Communication connection weights between them For the set of real numbers, The total number of agents. If the number of agents is... Intelligent agents can be acquired The information, ,otherwise .
[0025] Laplace matrix of multi-agent systems for: ; in, Let be the in-degree matrix. , It is a set of integers.
[0026] S11: Obtain the desired formation of the distributed multi-agent system.
[0027] The desired formation refers to the specified time-varying formation that a distributed multi-agent system expects to form. The agents in this application... The desired formation is .
[0028] S2: Based on the communication topology and the agent's state at the current moment, construct the communication triggering conditions for each agent.
[0029] The communication trigger condition is used to determine the trigger time; the trigger time is the moment when the current agent sends its corresponding state to neighboring agents. The formula for calculating the agent's communication trigger condition is as follows: ; in, For intelligent agents Communication triggering conditions, The total number of agents. Number the intelligent agents. For the first Trigger threshold adjustment parameters for each agent For edge-based time-varying adaptive gain, For intelligent agents With intelligent agents Communication connection weights between them For the first The state estimation error of an agent, i.e., the difference between the most recently triggered state and the actual state. , For local formation error, , For the first The state at the trigger moment of an agent For the first The desired formation of each agent For the first The state at the trigger moment of an agent For the first The desired formation of each agent For the first The actual state of each agent To balance the adjustable parameters of the communication frequency, , For preset time parameters, superscript Adjustable parameters for balancing system performance, .
[0030] Specifically, for intelligent agents When the triggering condition is met At that time, communication with neighboring agents is triggered, and the agents are... The state is sent to neighboring agents, and the current time is defined as the trigger time, and then... Increase by 1, that is ,in, For the trigger time, The number of communication triggers and the initialization time for communication triggers are specified. )satisfy , No. Triggering state of each agent and in the interval The internal temperature remains constant.
[0031] S3: Based on the agent's state at the triggering moment, the corresponding local formation error, the preset time parameters, and the time-varying adaptive gain, construct a preset time adaptive event triggering formation control law.
[0032] Specifically, the calculation formula for the preset time adaptive event-triggered formation control law is as follows: ; in, For intelligent agents The control input, For preset time parameters, , Used to realize the state of a distributed multi-agent system within a preset time. Convergence within, It is a constant used to adjust the rate of increase of the preset time parameter. The total number of agents. Number the intelligent agents. For intelligent agents With intelligent agents Communication connection weights between them For edge-based time-varying adaptive gain, the initial value is set to , , Local formation error, which is the difference between the agent's state at the trigger moment and the corresponding desired formation. For the derivative of the desired formation, For the first The state at the trigger moment of an agent and in the interval The internal remains constant. To adjust the positive definite symmetric parameters of the adaptive update rate, , This is the derivative of the edge-based time-varying adaptive gain.
[0033] S4: Real-time formation control is performed based on the dynamic model of a distributed multi-agent system, triggered by a preset time-adaptive event triggering the formation control law.
[0034] The dynamic model calculation formula is as follows: ; in, For intelligent agents The derivative of the state, For intelligent agents Control input.
[0035] Specifically, intelligent agents Control input (controller) The dynamic model of the directly driven distributed multi-agent system in this application uses a distributed controller that utilizes only the local errors of neighboring communicating agents, rather than global information. That is, when the agents... With intelligent agents When there is a communication relationship, the communication connection weight between the two parties. Local formation error Determine the control input This takes effect when there is no communication relationship; otherwise, the communication connection weight between the two is adjusted. Local formation error Determine the control input It doesn't work at times.
[0036] In one exemplary embodiment, the intelligent agent consists of four unmanned vehicles, forming a multi-unmanned vehicle system. In this system, a system centered on the origin is employed. A two-dimensional inertial coordinate system based on This describes the motion state of the autonomous vehicles. The desired formation of a multi-autonomous vehicle system is a square formation, and the communication topology is as follows: Figure 2 As shown, the expression for the square formation vector is as follows: ; in, For driverless cars The expected formation Number the intelligent agent.
[0037] Define the position coordinates of the autonomous vehicle :in, Let x be the x-coordinate of the driverless car. Let be the ordinate of the autonomous vehicle. Specifically, the initial position of the first autonomous vehicle is . The initial position of the second driverless car is The initial position of the third driverless car is The initial position of the fourth driverless car is .
[0038] Parameters defining the communication trigger condition: where, the first Trigger threshold adjustment parameters for individual autonomous vehicles Adjustable parameters for balanced communication frequencies Adjustable parameters for balancing system performance A constant used to adjust the rate of increase of the preset time parameter. Initial value of time-varying adaptive gain Set as The preset time is selected as .
[0039] Taking the speed of each unmanned vehicle as the control input, the... The kinematic model of the autonomous vehicle is described as follows: ; in, For the first The x-coordinate of the driverless car For the first The ordinate of an autonomous vehicle For along axial control input, For along Axial direction control input.
[0040] Using the method provided in this application, real-time platooning control of a multi-unmanned vehicle system is performed. After 20 seconds, the position trajectories of the unmanned vehicles are as follows: Figure 3 As shown, the circle symbol represents the initial position of the autonomous vehicle, and the square symbol represents the position of the autonomous vehicle 20 seconds later. Figure 3 As shown, the four driverless vehicles achieved the desired square formation.
[0041] The local formation error for each autonomous vehicle is calculated. The calculation formula is as follows: ; in, For the first The trigger moment status of an autonomous vehicle For the first The expected formation of an autonomous vehicle platoon. For the first The trigger moment status of an autonomous vehicle For the first The expected formation of an autonomous vehicle platoon. This refers to the number of driverless cars.
[0042] like Figure 4 As shown, the autonomous vehicle converges to zero within the specified 8 seconds. A schematic diagram of the autonomous vehicle's triggering time is also shown. It can be seen that the local formation error converges within the preset time, and the number of triggering times is relatively small. In summary, the multi-autonomous vehicle system achieves preset time event-triggered formation control, significantly saving communication resources.
[0043] Based on the same inventive concept, this application also provides an event-triggered formation control device for implementing the distributed multi-agent system described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more event-triggered formation control device embodiments for distributed multi-agent systems provided below can be found in the limitations of the event-triggered formation control method for distributed multi-agent systems described above, and will not be repeated here.
[0044] This application provides an event-triggered formation control device for a distributed multi-agent system, comprising: The communication topology determination module is used to obtain the communication topology and desired formation of the distributed multi-agent system. The communication triggering condition determination module is used to construct the communication triggering condition for each agent based on the communication topology and the agent state at the current moment; the communication triggering condition is used to determine the triggering time; the triggering time is the moment when the current agent sends the corresponding state to the neighboring agents; The control law construction module is used to construct a preset time adaptive event-triggered formation control law based on the agent's state at the triggering moment, the corresponding local formation error, the preset time parameters, and the time-varying adaptive gain; the local formation error is the difference between the agent's state at the triggering moment and the corresponding desired formation. The formation control module is used to trigger the formation control law according to the preset time adaptive event, and to perform real-time formation control based on the dynamic model of the distributed multi-agent system.
[0045] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores event-triggered formation control data for a distributed multi-agent system. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an event-triggered formation control method for a distributed multi-agent system.
[0046] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0047] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0048] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0049] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0051] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0052] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0053] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. An event-triggered formation control method of a distributed multi-agent system, characterized in that, The event-triggered formation control method of the distributed multi-agent system comprises the following steps: a communication topology of the distributed multi-agent system and a desired formation shape are acquired; a communication trigger condition of each agent is constructed according to the communication topology and a state of the agent at a current time; the communication trigger condition is used to determine a trigger time; the trigger time is a time at which a current agent sends a corresponding state to a neighboring agent; a preset time adaptive event-triggered formation control law is constructed according to the state of the agent at the trigger time, a corresponding local formation error, a preset time parameter and a time-varying adaptive gain; the local formation error is a difference between the state of the agent at the trigger time and the corresponding desired formation shape; real-time formation control is performed based on a dynamic model of the distributed multi-agent system according to the preset time adaptive event-triggered formation control law. 2.The event-triggered formation control method of distributed multi-agent system according to claim 1, wherein, The agent comprises a robot, a drone or an intelligent vehicle. 3.The event-triggered formation control method of distributed multi-agent system according to claim 1, wherein, The acquisition of the communication topology of the distributed multi-agent system and the desired formation shape comprises the following steps: Using the formula Determining a communication topology of a distributed multi-agent system ; wherein, is an agent is an agent is a communication connection weight between agents, is a real number set, is the total number of agents.
4. The event-triggered formation control method of distributed multi-agent system according to claim 1, wherein, The construction of the communication trigger condition of each agent according to the communication topology and the state of the agent at the current time comprises the following steps: Using the formula Constructing the communication trigger condition for each agent ; wherein, is the total number of agents, is the agent number, is the triggering threshold adjustment parameter of the th agent, is the edge-based time-varying adaptive gain, is the communication connection weight between the th agent and the th agent, is the state estimation error of the th agent, is the local formation error, , is the triggering time state of the th agent, is the desired formation of the th agent, is the triggering time state of the th agent, is the desired formation of the th agent, is the adjustable parameter for balancing the communication frequency, is the preset time parameter, and the superscript is the adjustable parameter for balancing the system performance.
5. The event-triggered formation control method of distributed multi-agent system according to claim 1, wherein, The construction of the preset time adaptive event-triggered formation control law according to the state of the agent at the trigger time, the corresponding local formation error, the preset time parameter and the time-varying adaptive gain comprises the following steps: Using the formula A preset time adaptive event-triggered platoon control law is constructed. wherein, is a control input for the agent, is a control input for the agent, is a preset time parameter, is a total number of agents, is an agent number, is a control input for the agent is a communication connection weight between the agent is a communication connection weight between the agent is an edge-based time-varying adaptive gain, is a local formation error, is a derivative of a desired formation shape.
6. The event-triggered formation control method of distributed multi-agent system according to claim 1, wherein, An expression of the dynamic model of the distributed multi-agent system is as follows: ； wherein, is the state of the agent is the derivative of the state of the agent is the control input of the agent is the control input of the agent 7. An event-triggered formation control device for a distributed multi-agent system, characterized in that, The event-triggered formation control device of the distributed multi-agent system comprises: a communication topology determination module configured to acquire a communication topology of the distributed multi-agent system and a desired formation shape; a communication trigger condition determination module configured to construct a communication trigger condition of each agent according to the communication topology and a state of the agent at a current time; the communication trigger condition is used to determine a trigger time; the trigger time is a time at which a current agent sends a corresponding state to a neighboring agent; a control law construction module configured to construct a preset time adaptive event-triggered formation control law according to a state of the agent at the trigger time, a corresponding local formation error, a preset time parameter and a time-varying adaptive gain; the local formation error is a difference between the state of the agent at the trigger time and the corresponding desired formation shape; a formation control module configured to perform real-time formation control based on a dynamic model of the distributed multi-agent system according to the preset time adaptive event-triggered formation control law.
8. A computer device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the event-triggered formation control method of the distributed multi-agent system according to any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the event-triggered formation control method of the distributed multi-agent system according to any one of claims 1-6.
10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the event-triggered formation control method of the distributed multi-agent system according to any one of claims 1-6.